Finance Webinar（2021-02)

Topic: How can Innovation Screening be Improved? A Machine Learning Analysis with Economic Consequences for Firm Prformance

Speaker: Xiang Zheng, Boston College

Time: Wednesday, 06 January,10:00-11:30 AM Beijing Time

Location: Microsoft Teams Online Conference Room

Abstract:

Using USPTO patent application data, I develop a machine-learning algorithm to analyze how the current patent examination process in the U.S. can be improved in terms of granting higher quality patents. I make use of the quasi-random assignment of patent applications to examiners to show that screening decisions aided by a machine learning algorithm lead to a 15.5% gain in patent generality. To further analyze the economic consequences of inefficient patent screening on both public and private firms, I construct an ex-ante measure of patent screening efficiency for each examiner by exploiting the disagreement in patent screening decisions between my algorithm and current patent examiner. I first show that patents granted by examiners with higher false acceptance rates have lower announcement returns around patent grant news. Moreover, these patents are more likely to expire early. Next, I find that public firms whose patents are granted by such examiners are more likely to get sued in patent litigation cases. Consequently, these firms cut R&D investments and have worse operating performance. Lastly, I find that private firms whose patents are granted by such examiners are less likely to exit successfully by an IPO or an M&A.

Introduction:

Xiang Zheng is a Ph.D. candidate in Finance at Boston College. His main research interests are FinTech, corporate innovation, equity offerings, and banking. His job market paper analyzes the economic consequence of inefficient patent screenings on firm performance using a machine learning approach.

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